Modelling Stem Diameter Variability in Pinus caribaea (Morelet) Plantations in South West Nigeria
2
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
Stem diameter variability is an essential inventory result that provides useful information in forest management decisions. Little has been done to explore the modelling potentials of standard deviation (SDD) and coefficient of variation (CVD) of diameter at breast height (dbh). This study, therefore, was aimed at developing and testing models for predicting SDD and CVD in stands of Pinus caribaea Morelet (pine) in south west Nigeria. Sixty temporary sample plots of size 20 m×20 m, ranging between 15 and 37 years were sampled, covering the entire range of pine in south west Nigeria. The dbh (cm), total and merchantable heights (m), number of stems and age of trees were measured within each plot. Basal area (m2), site index (m), relative spacing and percentile positions of dbh at 24th, 63rd, 76th and 93rd (i.e. P24, P63, P76 and P93) were computed from measured variables for each plot. Linear mixed model (LMM) was used to test the effects of locations (fixed) and plots (random). Six candidate models (3 for SDD and 3 for CVD), using three categories of explanatory variables (i.e. (i) only stand size measures, (ii) distribution measures, and (iii) combination of i and ii). The best model was chosen based on smaller relative standard error (RSE), prediction residual sum of squares (PRESS), corrected Akaike Information Criterion (AICc) and larger coefficient of determination (R2). The results of the LMM indicated that location and plot effects were not significant. The CVD and SDD models having only measures of percentiles (i.e. P24 and P93) as predictors produced better predictions than others. However, CVD model produced the overall best predictions, because of the lower RSE and stability in measuring variability across different stand developments. The results demonstrate the potentials of CVD in modelling stem diameter variability in relationship with percentiles variables.
Abstract
Stem diameter variability is an essential inventory result that provides useful information in forest management decisions. Little has been done to explore the modelling potentials of standard deviation (SDD) and coefficient of variation (CVD) of diameter at breast height (dbh). This study, therefore, was aimed at developing and testing models for predicting SDD and CVD in stands of Pinus caribaea Morelet (pine) in south west Nigeria. Sixty temporary sample plots of size 20 m×20 m, ranging between 15 and 37 years were sampled, covering the entire range of pine in south west Nigeria. The dbh (cm), total and merchantable heights (m), number of stems and age of trees were measured within each plot. Basal area (m2), site index (m), relative spacing and percentile positions of dbh at 24th, 63rd, 76th and 93rd (i.e. P24, P63, P76 and P93) were computed from measured variables for each plot. Linear mixed model (LMM) was used to test the effects of locations (fixed) and plots (random). Six candidate models (3 for SDD and 3 for CVD), using three categories of explanatory variables (i.e. (i) only stand size measures, (ii) distribution measures, and (iii) combination of i and ii). The best model was chosen based on smaller relative standard error (RSE), prediction residual sum of squares (PRESS), corrected Akaike Information Criterion (AICc) and larger coefficient of determination (R2). The results of the LMM indicated that location and plot effects were not significant. The CVD and SDD models having only measures of percentiles (i.e. P24 and P93) as predictors produced better predictions than others. However, CVD model produced the overall best predictions, because of the lower RSE and stability in measuring variability across different stand developments. The results demonstrate the potentials of CVD in modelling stem diameter variability in relationship with percentiles variables.
참고문헌 (32)
- Adedeji OH, "Spatial Pattern of Land Cover Change using Remotely Sensed Imagery and GIS : A Case Study of Omo-Shasha-Oluwa Forest Reserve, SW Nigeria(1986-2002)", Journal of Geographic Information System, 6, pp.375~385 (2014)
- Bailey RL, "Quantifying Diameter Distributions with Weibull Function", Forest Science, 19, pp.97~104 (1973)
- Loblolly Pine Growth and Yield Prediction for Managed West Gulf Plantations, "Baldwin VC Jr", USDA Forest Service, 27~, pp.Research Paper SO-236 (1987)
- Borders BE, "Percentile-Based Distributions Characterize Forest Stand Tables", Forest Science, 33, pp.570~576 (1987)
- Burnham KK, "Model selection and multimodel inference: a practical information-theoretic approach", pp.515~ (2002), Springer
- Cao QV, "Predicting Parameters of a Weibull Function for Modelling Diameter Distribution", Forest Science, 50, pp.682~685 (2004)
- Correlation Analysis for the Behavioral Sciences, "Applied Multiple Regression", pp.2003, Cohen J
- Eerikäinen K, "A percentile based basal area diameter distribution model for predicting the stand development of Pinus kesiya plantations in Zambia and Zimbabwe", Forest Ecology and Management, 172, pp.109~124 (2003)
- Fries C, "Silvicultural Models to Maintain and Restore Natural Stand Structures in Swedish Boreal Forest", Forest Ecology and Management, 94, pp.89~103 (1997)
- Hafley WL, "Statistical distributions for fitting diameter and height data in even-aged stands", Canadian Journal of Forest Research, 7, pp.481~487 (1977)
- Hyink DM, "A Generalized Framework for Projecting Forest Yield and Stand Structure using Diameter Distributions", Forest Science, 29, pp.85~95 (1983)
- Kilkki P, "Weibull Function in the Estimation of Basal Area Dbh Distribution", Silva Fennica, 20, pp.149~156 (1986)
- Knowe SA, "Basal Area and Diameter Distribution Prediction Equations for Young Douglas-Fir Plantations with Hardwood Competition : Coast Ranges", Western Journal of Applied Forestry, 20, pp.77~93 (2005)
- Koesmarno HK, "Recent Advances in Forest Mensuration and Growth and Yield Research", pp.45~61 (1995), Ministry of Environment and Energy Danish Forest and Landscape Research Institute
- Koesmarno HK, "Class-Size Percentile Transformation for Reconstructing a Distribution Function", Journal of Applied Statistics, 23, pp.423~434 (1996)
- Yield Tables and Stand Structure for Unthinned Long Leaf Pine Plantations in Louisiana and Texas, "Lohrey RE", USDA Forest Service, 53~, pp.Research Paper SO-133 (1977)
- Lumbres RIC, "Percentile-Based Weibull Diameter Distribution Model for Pinus kesiya Stands in Benguet Province, Philippines", Southern Forests: A Journal of Forest Science, 76, pp.117~123 (2014)
- Maltamo M, "Comparison of Percentile Based Prediction Method and the Weibull Distribution in Describing the Diameter Distribution of Heterogeneous Scots Pine Stands", Forest Ecology and Management, 133, pp.263~274 (2000)
- Maltamo M, "Comparing Basal Area Diameter Distributions Estimated by Tree Species and for the Entire Growing Stock in a Mixed Stand", Silva Fennica, 31, pp.53~65 (1997)
- Mc Elhinny C, "Forest and Woodland Stand Structural Complexity : Its Definition and Measurement", Forest Ecology and Management, 218, pp.1~24 (2005)
- McDonald JH, "Handbook of Biological Statistics", pp.305~ (2014), Sparky House Publishing
- Mehtätalo L, "Comparing Strategies for Modelling Tree Diameter Percentiles from Remeasured Plots", Environmetrics, 19, pp.529~548 (2008)
- Mehtatalo L., "Recovering Plot-Specific Diameter Distribution and Height-Diameter Curve using ALS Based Stand Characteristics", ISPRS Workshop on Laser Scanning and SilviLaser ESPOO, pp.288~293 (2007)
- Biodiversity Inventory of Omo Biosphere Reserve, Nigeria, "Ola-Adams BA", BRAAF, 315~ (1999)
- Oyebade BA, "Models for Growth Characteristics and their Applications in Yield Studies for Pins caribaea Morelet in 1851 in Southwestern Nigeria", Ph.D, pp.2014
- Recovering Parameters of Johnson’s SB Distribution, "Parresol BR.", Department of Agriculture, Forest Service, Southern Research Station, 9~, pp.U.S, Research Station Research Paper SRS-31 (2003)
- Poudel KP, "Evaluation of Methods to Predict Weibull Parameters for Characterizing Diameter Distributions", Forest Science, 59, pp.243~252 (2013)
- Royall R., "Statistical Evidence: A Likelihood Paradigm" (1997), Chapman and Hall
- Jouni Siipilehto, "Parameter recovery vs. parameter prediction for the Weibull distribution validated for Scots pine stands in Finland", Silva Fennica, 47(4) (2013), Finnish Society of Forest Science[DOI]
- Tewari VP, "Fitting a Bivariate Distribution to Diameter-Height Data of Forest Trees", Indian Forester, 123, pp.815~820 (1997)
- "Diameter Distribution Prediction Models for Thinned Slash and Loblolly Pine Plantations in the Southeast" (2009)[URL]
- Zeide B, "Diameter Variability in Loblolly Pine Plantations", Forest Ecology and Management, 128, pp.139~143 (2000)